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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

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Showing 176–200 of 1277 papers

TitleStatusHype
Incremental Sampling Without Replacement for Sequence ModelsCode1
Learn to Design the Heuristics for Vehicle Routing ProblemCode1
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
Targeted sampling of enlarged neighborhood via Monte Carlo tree search for TSPCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Graph Neural Networks for Maximum Constraint SatisfactionCode1
Exact Combinatorial Optimization with Graph Convolutional Neural NetworksCode1
A Cooperative Multi-Agent Reinforcement Learning Framework for Resource Balancing in Complex Logistics NetworkCode1
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree SearchCode1
Attention, Learn to Solve Routing Problems!Code1
Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization AlgorithmsCode1
Neural Combinatorial Optimization with Reinforcement LearningCode1
Generative Adversarial Networks in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
Deep Boltzmann Machines in Estimation of Distribution Algorithms for Combinatorial OptimizationCode1
Pointer NetworksCode1
Denoising Autoencoders for fast Combinatorial Black Box OptimizationCode1
LRM-1B: Towards Large Routing Model—0
Large Language Models for Combinatorial Optimization: A Systematic Review—0
Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability—0
On Training-Test (Mis)alignment in Unsupervised Combinatorial Optimization: Observation, Empirical Exploration, and AnalysisCode0
GreedyPrune: Retenting Critical Visual Token Set for Large Vision Language Models—0
Synthesizing Min-Max Control Barrier Functions For Switched Affine Systems—0
Large Language Models for Design Structure Matrix Optimization—0
Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization—0
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